{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6074d808-d3e5-4282-92e3-b3ae94f9cbcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Downloading namex-0.0.8-py3-none-any.whl (5.8 kB)\n",
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      "Installing collected packages: namex, libclang, flatbuffers, termcolor, tensorboard-data-server, optree, opt-einsum, ml-dtypes, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, keras, tensorflow-intel, tensorflow\n",
      "Successfully installed absl-py-2.1.0 astunparse-1.6.3 flatbuffers-24.3.25 gast-0.6.0 google-pasta-0.2.0 grpcio-1.66.2 keras-3.6.0 libclang-18.1.1 ml-dtypes-0.4.1 namex-0.0.8 opt-einsum-3.4.0 optree-0.13.0 tensorboard-2.17.1 tensorboard-data-server-0.7.2 tensorflow-2.17.0 tensorflow-intel-2.17.0 termcolor-2.5.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f7fe34c-a7fe-4f02-8d1b-235e754aa5ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m100,480\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mnist=tf.keras.datasets.mnist\n",
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
    "x_train,x_text=x_train/255.0,x_test/255.0\n",
    "\n",
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6b5b523-cc3b-49e0-9e9d-6a4e005a421d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m100,480\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "787b9c90-98f2-40ba-a73c-89a9179ca13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 872us/step - loss: 0.4294 - sparse_categorical_accuracy: 0.8806\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 880us/step - loss: 0.1225 - sparse_categorical_accuracy: 0.9642\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 884us/step - loss: 0.0784 - sparse_categorical_accuracy: 0.9762\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 843us/step - loss: 0.0568 - sparse_categorical_accuracy: 0.9831\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 849us/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9869\n",
      "313/313 - 0s - 921us/step - loss: 14.4787 - sparse_categorical_accuracy: 0.9738\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[14.478652000427246, 0.973800003528595]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam',metrics=['sparse_categorical_accuracy'])\n",
    "model.fit(x_train,y_train,batch_size=32,epochs=5) \n",
    "model.evaluate(x_test,y_test,batch_size=32,verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "673e82fc-8411-4c44-826d-55d9b889419c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    t=np.random.randint(1,10000)\n",
    "    x=tf.reshape(x_test[t],(1,28,28))\n",
    "    y_pred=np.argmax(model.predict(x),axis=1)\n",
    "    plt.subplot(1,5,i+1)\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(x_test[t],cmap='gray')\n",
    "    title=\"标签值:\"+str(y_test[t])+\"\\n预测值:\"+str(y_pred[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a847b28a-49cf-4142-b181-de9f05ff12d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
